{"uid":"cap_wJZTJhnO98QzSon0bSXSN","slug":"material-libraries-string-normalisation-date-by-category-pivot-d38c25d3","name":"Material Libraries — String Normalisation + Date-by-Category Pivot","description":"Material libraries — String normalisation + Date by category pivot: String normalisation + Date by category pivot for supplied material libraries records (material_count, texture_mb). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-creative-03-035?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","asset_group","date","material_count","texture_mb"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"texture_mb":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"asset_group":{"type":"string","default":"","maxLength":80},"material_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","texture_mb":1,"asset_group":"group-a","material_count":10},{"date":"2026-10-02","row_id":"row-2","texture_mb":2,"asset_group":"group-b","material_count":20},{"date":"2026-10-03","row_id":"row-3","texture_mb":3,"asset_group":"group-a","material_count":15},{"date":"2026-10-04","row_id":"row-4","texture_mb":4,"asset_group":"group-b","material_count":30}],"maxItems":200,"minItems":1},"field":{"enum":["material_count","texture_mb"],"type":"string","default":"material_count"}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_YeR5iXO5B4ZlSFUEovSkV","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Runs two local analytical reports on material library records: string normalisation and a date-by-category pivot, covering up to 200 rows of material_count or texture_mb data.","exampleAgentPrompt":"Can you run the string normalisation and date-by-category pivot reports on my material library data? I have 4 rows with asset groups 'group-a' and 'group-b', dates from 2026-10-01 to 2026-10-04, and I want to analyse the material_count field.","exampleUseCases":[{"title":"3D artist material count audit","prompt":"I have a list of 50 material library records with asset groups and dates — can you normalise the strings and give me a pivot of material_count by asset group and date so I can see which groups are heaviest?"},{"title":"Texture budget analysis by group","prompt":"Run both analytical reports on my texture_mb data for these 120 material library rows — I need the string normalisation pass and a date-by-category pivot grouped by asset_group."},{"title":"Quality-check creative asset records","prompt":"I've got some messy material library entries with inconsistent string formatting across about 80 rows — can you normalise them and also produce a date pivot on the material_count field broken down by category?"}],"resultDescription":"Returns two analytical reports: (1) a string-normalisation report cleaning and standardising text fields in the supplied material library rows, and (2) a date-by-category pivot table aggregating the chosen numeric field (material_count or texture_mb) across asset_group and date dimensions — all computed locally with no external lookups.","failureModes":["Rows array empty or missing — validation error returned","More than 200 rows submitted — request rejected with item-limit error","Invalid date format (not YYYY-MM-DD) — pattern validation failure","Numeric fields out of allowed range — schema rejection","Unknown field enum value — enum validation error","Malformed row_id (empty string or exceeds 80 chars) — validation error"],"whenToPreferThis":"Choose this endpoint when you have up to 200 material library records with asset_group, date, material_count, and texture_mb fields and need both a string normalisation pass and a date-by-category pivot in a single low-cost call. It is ideal for offline, supplied-data analytics with no need for live lookups or external enrichment, and costs only $0.01 USDC for both reports together.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-22T00:49:04.110Z","isFirstParty":false,"canonicalSlug":"material-libraries-string-normalisation-date-by-category-pivot-d38c25d3"}